Targeted metabolomic analyses were performed for cell culture supernatants as well as for cell lysates. For extraction of intracellular metabolites freeze/thaw cycles after resuspension in phosphate buffer were used.

Commercially available AbsoluteIDQ KIT plates were used for the quantification of amino acids, hexose, and biogenic amines. This fully automated assay is based on PITC (phenylisothiocyanate) derivatization in the presence of internal standards followed by LC-MS/MS detection using a mass spectrometer with electrospray ionization.

Producer vs. Mock Cell Line

Both CHO cell lines were cultivated as a fed-batch process lasting for 14 days in serum-free, chemically defined in-house medium in a 15 L bioreactor. Sampling for subsequent metabolite quantification took place at various time points throughout cultivation.

In addition to the cell-line comparison, it was possible to characterize different growth phases during the standard production process. Major metabolic distinctions were observable between the exponential phase (up to and including day 7) and the later phase compromising stationary and apoptotic phases for both cell lines.

To compare producer and mock cell-line cultivations, multivariate data analysis using a PLS model on all quantified compounds was employed. Changes between different sampling points, which represent the different stages of cultivation, showed stronger influence on the prediction model than overall alterations between the tested cell lines.

Therefore, differences between tested cell lines have to be evaluated for each phase individually as no overall variation was detectable.

Major variance between producer and mock cell lines within the individual cultivation phases was detected for intracellular nucleotide sugar concentrations, as shown in Figure 3.

Nucleotide sugars play an important key role in the mammalian glycosylation pathway. Increased glycosylation processing in producer cells seemed to have led to higher intracellular concentrations of required precursors for glycan structures due to increased stimulation of the overall glycosylation machinery.

Alternatively, increased consumption by producer cells should have led to lower concentrations or even to limiting bottlenecks. Increased amounts of intracellular nucleotide sugars at late stage for both cell lines were evident, but an adequate explanation with respect to cell and process knowledge was not found.

Moreover, specific consumption/production rates of diverse metabolites were different between producer and mock cell lines within the early phase (exponential phase comprising sampling between day 3–7). Mostly, amino acids were affected. In general, mock cells showed a slight increase in energy demand reflected by higher specific consumption rates. Thus, especially the slightly higher growth rate of mock cells seemed to be of relevance resulting in differentiation between proliferating and producing cells.

Conclusion

In this study, we detected expected differences between a producer and its corresponding mock cell line and thus we were able to prove the applicability of intracellular quantification of metabolites for bioprocess development.

Metabolomic analyses might also detect variations between different host cell lines encoding for the same recombinant protein or metabolic differences due to modified process settings. Potential biomarkers could be identified and used for systematic cell line and clone selection.

Furthermore, intracellular metabolite quantification might identify bottlenecks in protein production or processing that are not noticeable with current standardized monitoring and spent media analysis.

This expertise will be used for further improvement of our process-development strategies in order to deliver efficient and robust processes yielding high amounts of recombinant proteins with the desired product quality.

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